"""simple docstring"""


import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


a				=logging.getLogger(__name__)






class   __UpperCAmelCase		(      __lowerCAmelCase      ):

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None ):
										lowerCamelCase__   =self.layer[current_layer](_lowerCamelCase      ,					_lowerCamelCase      ,					head_mask[current_layer] )

										lowerCamelCase__   =layer_outputs[0]

										return hidden_states






@add_start_docstrings(
    '''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.''' , __lowerCAmelCase , )
class   __UpperCAmelCase		(      __lowerCAmelCase      ):

				def __init__( self      ,					_lowerCamelCase ):
										super().__init__(_lowerCamelCase )

										lowerCamelCase__   =BertEncoderWithPabee(_lowerCamelCase )

										self.init_weights()
										lowerCamelCase__   =0
										lowerCamelCase__   =0
										lowerCamelCase__   =0

										lowerCamelCase__   =0

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =threshold

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =patience

				def 						_a      ( self ):
										lowerCamelCase__   =0
										lowerCamelCase__   =0

				def 						_a      ( self ):
										lowerCamelCase__   =self.inference_layers_num / self.inference_instances_num
										lowerCamelCase__   =(
										    F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
										    F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
										)
										print(_lowerCamelCase )






				@add_start_docstrings_to_model_forward(_lowerCamelCase )
				def 						_a      ( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=False      ,					):

										if input_ids is not None and inputs_embeds is not None:
																raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time" )
										elif input_ids is not None:
																lowerCamelCase__   =input_ids.size()
										elif inputs_embeds is not None:
																lowerCamelCase__   =inputs_embeds.size()[:-1]
										else:
																raise ValueError("You have to specify either input_ids or inputs_embeds" )

										lowerCamelCase__   =input_ids.device if input_ids is not None else inputs_embeds.device

										if attention_mask is None:
																lowerCamelCase__   =torch.ones(_lowerCamelCase      ,					device=_lowerCamelCase )
										if token_type_ids is None:
																lowerCamelCase__   =torch.zeros(_lowerCamelCase      ,					dtype=torch.long      ,					device=_lowerCamelCase )

										# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
										# ourselves in which case we just need to make it broadcastable to all heads.
										lowerCamelCase__   =self.get_extended_attention_mask(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )

										# If a 2D ou 3D attention mask is provided for the cross-attention
										# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
										if self.config.is_decoder and encoder_hidden_states is not None:
																lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =encoder_hidden_states.size()
																lowerCamelCase__   =(encoder_batch_size, encoder_sequence_length)
																if encoder_attention_mask is None:
																						lowerCamelCase__   =torch.ones(_lowerCamelCase      ,					device=_lowerCamelCase )
																lowerCamelCase__   =self.invert_attention_mask(_lowerCamelCase )
										else:
																lowerCamelCase__   =None

										# Prepare head mask if needed
										# 1.0 in head_mask indicate we keep the head
										# attention_probs has shape bsz x n_heads x N x N
										# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
										# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
										lowerCamelCase__   =self.get_head_mask(_lowerCamelCase      ,					self.config.num_hidden_layers )

										lowerCamelCase__   =self.embeddings(
										    input_ids=_lowerCamelCase      ,					position_ids=_lowerCamelCase      ,					token_type_ids=_lowerCamelCase      ,					inputs_embeds=_lowerCamelCase )
										lowerCamelCase__   =embedding_output

										if self.training:
																lowerCamelCase__   =[]
																for i in range(self.config.num_hidden_layers ):
																						lowerCamelCase__   =self.encoder.adaptive_forward(
																						    _lowerCamelCase      ,					current_layer=_lowerCamelCase      ,					attention_mask=_lowerCamelCase      ,					head_mask=_lowerCamelCase )

																						lowerCamelCase__   =self.pooler(_lowerCamelCase )
																						lowerCamelCase__   =output_layers[i](output_dropout(_lowerCamelCase ) )
																						res.append(_lowerCamelCase )
										elif self.patience == 0:  # Use all layers for inference
																lowerCamelCase__   =self.encoder(
																    _lowerCamelCase      ,					attention_mask=_lowerCamelCase      ,					head_mask=_lowerCamelCase      ,					encoder_hidden_states=_lowerCamelCase      ,					encoder_attention_mask=_lowerCamelCase      ,					)
																lowerCamelCase__   =self.pooler(encoder_outputs[0] )
																lowerCamelCase__   =[output_layers[self.config.num_hidden_layers - 1](_lowerCamelCase )]
										else:
																lowerCamelCase__   =0
																lowerCamelCase__   =None
																lowerCamelCase__   =0
																for i in range(self.config.num_hidden_layers ):
																						calculated_layer_num += 1
																						lowerCamelCase__   =self.encoder.adaptive_forward(
																						    _lowerCamelCase      ,					current_layer=_lowerCamelCase      ,					attention_mask=_lowerCamelCase      ,					head_mask=_lowerCamelCase )

																						lowerCamelCase__   =self.pooler(_lowerCamelCase )
																						lowerCamelCase__   =output_layers[i](_lowerCamelCase )
																						if regression:
																												lowerCamelCase__   =logits.detach()
																												if patient_result is not None:
																																		lowerCamelCase__   =patient_result.detach()
																												if (patient_result is not None) and torch.abs(patient_result - labels ) < self.regression_threshold:
																																		patient_counter += 1
																												else:
																																		lowerCamelCase__   =0
																						else:
																												lowerCamelCase__   =logits.detach().argmax(dim=1 )
																												if patient_result is not None:
																																		lowerCamelCase__   =patient_result.detach().argmax(dim=1 )
																												if (patient_result is not None) and torch.all(labels.eq(_lowerCamelCase ) ):
																																		patient_counter += 1
																												else:
																																		lowerCamelCase__   =0

																						lowerCamelCase__   =logits
																						if patient_counter == self.patience:
																												break
																lowerCamelCase__   =[patient_result]
																self.inference_layers_num += calculated_layer_num
																self.inference_instances_num += 1

										return res






@add_start_docstrings(
    '''Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. ''' , __lowerCAmelCase , )
class   __UpperCAmelCase		(      __lowerCAmelCase      ):

				def __init__( self      ,					_lowerCamelCase ):
										super().__init__(_lowerCamelCase )
										lowerCamelCase__   =config.num_labels

										lowerCamelCase__   =BertModelWithPabee(_lowerCamelCase )
										lowerCamelCase__   =nn.Dropout(config.hidden_dropout_prob )
										lowerCamelCase__   =nn.ModuleList(
										    [nn.Linear(config.hidden_size      ,					self.config.num_labels ) for _ in range(config.num_hidden_layers )] )

										self.init_weights()






				@add_start_docstrings_to_model_forward(_lowerCamelCase )
				def 						_a      ( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					):
										lowerCamelCase__   =self.bert(
										    input_ids=_lowerCamelCase      ,					attention_mask=_lowerCamelCase      ,					token_type_ids=_lowerCamelCase      ,					position_ids=_lowerCamelCase      ,					head_mask=_lowerCamelCase      ,					inputs_embeds=_lowerCamelCase      ,					output_dropout=self.dropout      ,					output_layers=self.classifiers      ,					regression=self.num_labels == 1      ,					)

										lowerCamelCase__   =(logits[-1],)

										if labels is not None:
																lowerCamelCase__   =None
																lowerCamelCase__   =0
																for ix, logits_item in enumerate(_lowerCamelCase ):
																						if self.num_labels == 1:
																												#  We are doing regression
																												lowerCamelCase__   =MSELoss()
																												lowerCamelCase__   =loss_fct(logits_item.view(-1 )      ,					labels.view(-1 ) )
																						else:
																												lowerCamelCase__   =CrossEntropyLoss()
																												lowerCamelCase__   =loss_fct(logits_item.view(-1      ,					self.num_labels )      ,					labels.view(-1 ) )
																						if total_loss is None:
																												lowerCamelCase__   =loss
																						else:
																												total_loss += loss * (ix + 1)
																						total_weights += ix + 1
																lowerCamelCase__   =(total_loss / total_weights,) + outputs

										return outputs



"""simple docstring"""


import random





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = False							)				->   dict:




						'''simple docstring'''



						lowerCamelCase__   ={i: [] for i in range(__lowerCAmelCase							)}

						# if probability is greater or equal than 1, then generate a complete graph
						if probability >= 1:
												return complete_graph(__lowerCAmelCase							)
						# if probability is lower or equal than 0, then return a graph without edges
						if probability <= 0:
												return graph

						# for each couple of nodes, add an edge from u to v
						# if the number randomly generated is greater than probability probability
						for i in range(__lowerCAmelCase							):
												for j in range(i + 1  ,       __lowerCAmelCase							):
																		if random.random() < probability:
																								graph[i].append(__lowerCAmelCase							)
																								if not directed:
																														# if the graph is undirected, add an edge in from j to i, either
																														graph[j].append(__lowerCAmelCase							)
						return graph





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   dict:




						'''simple docstring'''



						return {
						    i: [j for j in range(__lowerCAmelCase							) if i != j] for i in range(__lowerCAmelCase							)
						}


if __name__ == "__main__":
	import doctest

	doctest.testmod()


